*Article* **Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation**

### **Zhengwei Qu, Chenglin Xu, Kai Ma \* and Zongxu Jiao**

School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China **\***Correspondence:kma@ysu.edu.cn;Tel.: +86-335-838-7556

 Received: 4 June 2019; Accepted: 24 June 2019; Published: 26 June 2019

**Abstract:** In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

**Keywords:** automatic generation control; fuzzy neural network control; thermostatically controlled loads; back propagation algorithm; particle swarm optimization
